Outcome-Based Pricing for New Pharmaceuticals via Rebates

The price of new brand-name prescription drugs has been rising fast in the United States. For example, the Amgen cholesterol drug Repatha had an initial list price of $14,523 per year. Patients, even with insurance coverage, must pay out of pocket a significant portion of this price. The treatment might not be successful, and this possibility reduces risk-sensitive patients’ incentives to purchase the drug. The high price together with the chance of negative treatment outcomes may lead payers to deny coverage for the drug. Outcome-based pricing has been proposed as a way to reallocate the risks and improve both payer resource allocation and patient access to drugs. According to an outcome-based rebate contract between Amgen and Harvard Pilgrim Healthcare, if a patient on Repatha suffers a heart attack or a stroke, both patient and insurer are refunded the cost of the drug. We use a stylized model to analyze the effect of outcome-based pricing via rebates. Our model captures the interaction between heterogenous, price-sensitive, risk-sensitive patients who decide whether to purchase the drug; a payer deciding whether to provide coverage for the drug; and a price-setting pharmaceutical firm seeking to maximize expected profits. We find that, in many cases, a pharmaceutical firm and payer cannot simultaneously benefit from outcome-based pricing, and who will benefit is determined by the probability of treatment success. Outcome-based pricing thus appears unlikely to solve the issues of high drug prices and high payer expenditures. However, supplementing outcome-based pricing with a transfer payment from firm to payer can make payer and firm (but not necessarily the patients) better off than under uniform pricing when the drug has a low chance of success. This paper was accepted by Stefan Scholtes, healthcare management.

Kenya in the 1980s

This chapter describes the interplay between foreign aid and state policies in Kenya that together contributed to the emergence of a small yet robust locally owned pharmaceutical sector. Most important was a “ration kits” program that helped rationalize the procurement and distribution of drugs in rural areas. As part of that program, a government-funded component was used to specifically purchase locally produced drugs. This proved critical for the emergence and growth of the Kenyan pharmaceutical sector. Other policies in support of the state-owned pharmaceutical firm also indirectly pushed for and later assisted privately owned pharmaceutical firms. With the support of foreign aid, then, the Kenyan government was able to create a market for local producers. Foreign assistance did not come with technology transfer (mentoring), and access to technical know-how was predominantly available to Kenyans of Indian origin, whose social position during colonialism and after independence granted them educational, commercial, and cultural ties abroad. Moreover, there was little attention to quality standards (monitoring).

Managing Uncertainty in Drug Approval

This chapter considers management of uncertainty in drug approval. In the United States, the approval process of the Food and Drug Administration (FDA) determines whether a drug can legally be sold within the country. A similar process occurs in the European Union, with approval performed by the European Medicines Agency. To obtain approval for a new drug, a pharmaceutical firm must provide to the FDA information on treatment response through the performance of randomized trials that compare the new drug with an existing treatment or a placebo. The FDA makes a binary (yes/no) approval decision after reviewing the findings of these trials. Approval decisions are made with incomplete knowledge of the effectiveness and side effects of new drugs. The chapter describes how the FDA deals with uncertainty and suggests how the approval process might be improved.

An inquiry into the productivity of the Indian pharmaceutical industry: application of data envelopment analysis

Pharmaceutical industry is an important and significant industry in India. The common practice by experts, would-be investors and stakeholders of a pharmaceutical firm is to observe the year-end or quarterly financial figures of a firm and then use them to assess the firm’s future growth and competitive standing against rivals. However, over the past few years, there is a strange environment in which paid consultants and scamsters are making false and conflicting claims in terms of the performance and future growth of the companies. The researchers, however, have attempted, by means of Data Envelopment Analysis (DEA), to get past all this by benchmarking the companies based on the conversion from input to outputs. The advantage of using DEA is that, by simply using the figures from the financial reports, it brings a more rigorous quantitative analysis to make a comparison of the peers with the best virtual firm in their neighborhood. The technique itself may suggest measures for improvement. It is illustrated in the analysis by observing the slacks and targets about various companies of the pharmaceutical sector, i.e., decision making units (DMU).